#Load Libraries
rm(list = ls())
library(plyr)
library(tidyverse)
## ── Attaching packages ──────────────────────────────── tidyverse 1.2.1 ──
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## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0
## Warning: package 'ggplot2' was built under R version 3.5.2
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## ✖ dplyr::id() masks plyr::id()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::mutate() masks plyr::mutate()
## ✖ dplyr::rename() masks plyr::rename()
## ✖ dplyr::summarise() masks plyr::summarise()
## ✖ dplyr::summarize() masks plyr::summarize()
library(magrittr)
##
## Attaching package: 'magrittr'
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##
## set_names
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##
## extract
library(tibble)
library(stringi)
## Warning: package 'stringi' was built under R version 3.5.2
library(pomp)
## Warning: package 'pomp' was built under R version 3.5.2
## Welcome to pomp version 2!
## For information on upgrading your pomp version < 2 code, see the
## 'pomp version 2 upgrade guide' at https://kingaa.github.io/pomp/.
##
## Attaching package: 'pomp'
## The following object is masked from 'package:purrr':
##
## map
library(xtable)
## Warning: package 'xtable' was built under R version 3.5.2
#library(panelPomp)
#library(foreach)
#library(iterators)
#library(doRNG)
#library(aakmisc) ## available at https://kingaa.github.io/
stopifnot(packageVersion("pomp")>="2.2")
#stopifnot(packageVersion("panelPomp")>="0.9.1")
#stopifnot(packageVersion("aakmisc")>="0.26.2")
options(
stringsAsFactors=FALSE,
keep.source=TRUE,
encoding="UTF-8"
)
set.seed(407958184)
source("load_libraries_essential.R")
source("rahul_theme.R")
library(zoo)
## Warning: package 'zoo' was built under R version 3.5.2
##
## Attaching package: 'zoo'
## The following object is masked from 'package:pomp':
##
## time<-
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(stringr)
We fit a modified SEIR model to case data from the 2019-nCoV epidemic in NYC using case data from () to ().
full_model_name = "NYC_Covid_Model_Hyrbid_Model_1_Pre-Symp_Compartment_Set_b_p_to_0"
model_name = "N_12"
rda_index = 0
rds_index = 0
M = 5
V = 13
K = 14
#Load Observed NYC case data
Observed_data = read.csv(paste0(
"../Generated_Data/observed_data_",
model_name, ".csv"))
head(Observed_data)
## Y times obs_prop_positive
## 1 0 1 NA
## 2 0 2 0.00000000
## 3 2 3 0.25000000
## 4 2 4 0.05555556
## 5 7 5 0.15555556
## 6 0 6 0.00000000
### Define start date
true_start_date = as.Date("2020-03-01")
t0 = 0
start_of_year = as.Date("2020-01-01")
first_saturday_in_year = as.Date("2020-01-04")
## Compartment/Queue Cohort Numbers
M = 5
V = 13
K = 14
#Declare Csnippets and data
source("Csnippet_nyc_coronavirus_model_N_12.R")
## Load NYC covariate data
covariate_df = read.csv(file =
paste0("../Generated_Data/covariate_data_",
model_name, ".csv"))
### Create covariate table
covar=covariate_table(
time=covariate_df$times,
L_advanced_2_days=covariate_df$L_advanced_2_days,
F_w_y = covariate_df$F_w_y,
L_orig = covariate_df$L_orig,
w = covariate_df$Week,
y = covariate_df$Year,
times="time"
)
big_b_a_MLE = read.csv("../Generated_Data/Profiles/N_12_Model/top_2_LL_data/Man_Table_data/b_a_profile_top_2_LL_via_case_and_antibody_LL_big_b_a_parameter_combination_for_simulation.csv")
big_b_a_MLE
## X b_a M_0 V_0 K_0 R_0 b_q b_p p_S p_H_cond_S
## 1 1 0.9655172 5 13 14 3.083236 0.163873 0.9865475 0.154388 0.1735144
## phi_E phi_U phi_S h_V gamma N_0 E_0 z_0 C_0
## 1 1.09 1.09 0.2 0.125 11.72791 8e+06 54806.41 11625.01 0
## social_distancing_start_time quarantine_start_time PCR_sens sigma_M
## 1 17 22 0.9 0.27583
## beta_w_3 beta_w_2 beta_w_1 beta_w_0 g_0 g_F sigma_epsilon
## 1 0.01215073 0.9810086 -37.23481 229.4094 1183.3 0.1162005 109.1121
## G_w_y_scaling msg iter_num param_index loglik nfail trace_num
## 1 0.162 mif1 1 478 -628.7638 NA NA
## loglist.se Antibody_Mean_LL Antibody_LL_SE Median_Herd_Immunity
## 1 0.01206257 -24.50512 0.001865621 0.2021783
## combo_num sim_subset_index duration_of_symp_1 duration_of_symp_2
## 1 94 1 5 0.08526666
## duration_of_symp gamma_total Beta R_0_P R_0_A R_0_S_1
## 1 5.085267 0.1966465 0.6063076 0.5487626 2.475108 0.4680332
## R_0_S_2 R_0_NGM
## 1 0.006596615 3.498501
big_b_a_MLE_comb = big_b_a_MLE %>%
dplyr::select(-msg, -iter_num, -param_index,
-loglik, -nfail, -trace_num, -loglist.se,
-Antibody_Mean_LL, -Antibody_LL_SE,
-Median_Herd_Immunity, -combo_num,
-sim_subset_index, -duration_of_symp_1,
-duration_of_symp_2, -duration_of_symp,
-gamma_total, -Beta,
-R_0_P, -R_0_A, -R_0_S_1, -R_0_S_2,
-R_0_NGM)
##Simulation from ML
sim_data_big_b_a = simulate(nsim = 101,
seed = 12345,
times = Observed_data$times,
t0 = t0,
rprocess = pomp::euler(rproc,delta.t = 1),
params = big_b_a_MLE_comb,
paramnames = paramnames,
statenames = statenames,
obsnames = obsnames,
accumvars = acumvarnames,
rinit = init,
rmeas = rmeas,
partrans = par_trans,
covar = covar,
format = "data.frame")
#head(sim_data)
sim_data_big_b_a_traj_data = sim_data_big_b_a %>%
dplyr::select(time, .id, Y)
p = ggplot(data = sim_data_big_b_a_traj_data,
aes(x = time,
y = Y,
color = .id)) +
geom_point() + geom_line(aes(group = .id)) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
rahul_man_figure_theme +
theme(legend.position = "None")
p
png("../Figures/Representative_Simulations/big_b_a_all_traj_sim.png")
print(p)
dev.off()
## quartz_off_screen
## 2
select_trajectories = filter(sim_data_big_b_a, .id %in% seq(from = 5, to = 10))
select_trajectories = dplyr::select(select_trajectories, time, .id, Y)
select_trajectories$type = "Sim"
library(RColorBrewer)
full_blue_pallete = brewer.pal(9, "Blues")
sim_traj_pallete = full_blue_pallete[9:4]
sim_traj_scale = scale_color_manual(name = "Legend", values = c( sim_traj_pallete), labels = c("Sim_Traj_1", "Sim_Traj_2", "Sim_Traj_3", "Sim_Traj_4", "Sim_Traj_5", "Sim_Traj_6"))
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) + rahul_theme + rahul_man_figure_theme + theme_white_background +
sim_traj_scale
p
png("../Figures/Representative_Simulations/big_b_a_5_traj_sim.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) + rahul_theme +
rahul_man_figure_theme + theme_white_background +
sim_traj_scale + geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red')
p
png("../Figures/Representative_Simulations/big_b_a_5_traj_sim_with_obs.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) +
theme_white_background +
sim_traj_scale +
facet_wrap(~.id, ncol = 1) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
theme(legend.position = "None") +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
p
png("../Figures/Representative_Simulations/big_b_a_5_traj_sim_with_obs_facet.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) +
theme_white_background +
sim_traj_scale +
facet_wrap(~.id, ncol = 1) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
theme(legend.position = "None")
p
(One that corresponds well with the data by eye)
big_b_a_single_traj = select_trajectories %>%
filter(.id == "10")
p = ggplot(data = big_b_a_single_traj,
aes(x = time, y = Y)) + geom_point(size = 2,
color = 'blue') +
geom_line(color = 'blue') + rahul_man_figure_theme +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red')
p
png("../Figures/Representative_Simulations/big_b_a_best_traj_vs_obs.png")
print(p)
dev.off()
## quartz_off_screen
## 2
NYC_full_testing_data =
read.csv("../Generated_Data/NYC_full_testing_data.csv")
head(NYC_full_testing_data)
## Test.Date New_Positives Cumulative_Number_of_Positives
## 1 03/02/2020 0 0
## 2 03/03/2020 0 0
## 3 03/04/2020 2 2
## 4 03/05/2020 2 4
## 5 03/06/2020 7 11
## 6 03/07/2020 0 11
## Total_Number_of_Tests_Performed Cumulative_Number_of_Tests_Performed
## 1 0 0
## 2 8 8
## 3 8 16
## 4 36 52
## 5 45 97
## 6 64 161
## Prop_Pos Not_Pos Date
## 1 NA 0 2020-03-02
## 2 0.00000000 8 2020-03-03
## 3 0.25000000 6 2020-03-04
## 4 0.05555556 34 2020-03-05
## 5 0.15555556 38 2020-03-06
## 6 0.00000000 64 2020-03-07
nyc_rel_testing_data = NYC_full_testing_data %>%
mutate(times = as.numeric(as.Date(Date) - true_start_date)) %>%
select(times,
Total_Number_of_Tests_Performed =
Total_Number_of_Tests_Performed)
big_b_a_single_traj_data = big_b_a_single_traj %>%
dplyr::select(Y =Y, times = time) %>%
join(nyc_rel_testing_data) %>%
mutate(obs_prop_positive = Y/Total_Number_of_Tests_Performed) %>%
dplyr::select(-Total_Number_of_Tests_Performed)
## Joining by: times
big_b_a_single_traj_data$obs_prop_positive[1] = NA
big_b_a_single_traj_data
## Y times obs_prop_positive
## 1 0 1 NA
## 2 0 2 0.00000000
## 3 1 3 0.12500000
## 4 0 4 0.00000000
## 5 3 5 0.06666667
## 6 8 6 0.12500000
## 7 11 7 0.17460317
## 8 15 8 0.11538462
## 9 30 9 0.16949153
## 10 22 10 0.12643678
## 11 26 11 0.07008086
## 12 54 12 0.07814761
## 13 182 13 0.35616438
## 14 132 14 0.16019417
## 15 289 15 0.32435466
## 16 310 16 0.14065336
## 17 1566 17 0.46830144
## 18 1300 18 0.26837325
## 19 1195 19 0.23537522
## 20 1363 20 0.19676628
## 21 3179 21 0.48064711
## 22 3733 22 0.69296454
## 23 5408 23 0.95110798
## 24 4619 24 0.66412653
## 25 3283 25 0.47421638
## 26 3243 26 0.40802718
## 27 3211 27 0.44584838
## 28 6236 28 1.00564425
## 29 6012 29 0.60776385
## 30 4645 30 0.63361069
## 31 5537 31 0.73698922
## 32 6506 32 0.69486276
## 33 7563 33 0.67238620
## 34 4923 34 0.63002304
## 35 6839 35 0.84089512
## 36 6747 36 0.75309744
## 37 4989 37 0.47190692
## 38 5744 38 0.53702319
## 39 2732 39 0.24775551
## 40 4991 40 0.42433260
## 41 4069 41 0.37735324
## 42 2762 42 0.34297777
## 43 4502 43 0.51687715
## 44 5301 44 0.32702036
## 45 4605 45 0.36769403
## 46 3848 46 0.32732222
## 47 4428 47 0.38005321
## 48 3402 48 0.33122383
## 49 5659 49 0.72569890
## 50 3178 50 0.35995016
## 51 2813 51 0.24993336
## 52 3722 52 0.27850943
## 53 2576 53 0.14220259
## 54 2383 54 0.13824110
## 55 2321 55 0.17111472
## 56 2826 56 0.26809601
## 57 2370 57 0.23958755
## 58 2811 58 0.19982939
## 59 1143 59 0.08014304
## 60 1931 60 0.14310064
## 61 1485 61 0.09026258
## 62 1254 62 0.09386930
## 63 1085 63 0.11815311
## 64 2151 64 0.19885366
## 65 1102 65 0.08447034
## 66 2190 66 0.13378948
## 67 1700 67 0.11067708
## 68 1239 68 0.07901282
## 69 1671 69 0.12769372
## 70 764 70 0.07724194
## 71 1311 71 0.13732062
## 72 1539 72 0.10431777
## 73 1297 73 0.07029810
## 74 1476 74 0.07464725
## 75 1853 75 0.10081062
## 76 724 76 0.04745674
## 77 925 77 0.08413680
## 78 1463 78 0.11211587
## 79 1241 79 0.07960742
## 80 881 80 0.04165879
## 81 1409 81 0.07333195
## 82 678 82 0.03242623
## 83 889 83 0.04047348
## 84 707 84 0.04426774
## 85 738 85 0.04636261
## 86 550 86 0.03138553
## 87 1026 87 0.02997196
## 88 789 88 0.02314054
## 89 654 89 0.02288474
write.csv(big_b_a_single_traj_data,
"../Generated_Data/Representative_Simulations/big_b_a_single_sim_traj_data.csv",
row.names = FALSE)
small_b_a_MLE = read.csv("../Generated_Data/Profiles/N_12_Model/top_2_LL_data/Man_Table_data/b_a_profile_top_2_LL_via_case_and_antibody_LL_small_b_a_parameter_combination_for_simulation.csv")
small_b_a_MLE
## X b_a M_0 V_0 K_0 R_0 b_q b_p p_S p_H_cond_S
## 1 1 0.06896552 5 13 14 6.098822 0.2343746 0.940249 0.148861 0.1569547
## phi_E phi_U phi_S h_V gamma N_0 E_0 z_0 C_0
## 1 1.09 1.09 0.2 0.125 6.333218 8e+06 63566.34 13443.03 0
## social_distancing_start_time quarantine_start_time PCR_sens sigma_M
## 1 17 22 0.9 0.2709327
## beta_w_3 beta_w_2 beta_w_1 beta_w_0 g_0 g_F sigma_epsilon
## 1 0.01215073 0.9810086 -37.23481 229.4094 1183.3 0.1162005 109.1121
## G_w_y_scaling msg iter_num param_index loglik nfail trace_num
## 1 0.162 mif1 2 44 -627.43 NA NA
## loglist.se Antibody_Mean_LL Antibody_LL_SE Median_Herd_Immunity
## 1 0.008839892 -24.41689 0.002390176 0.1998329
## combo_num sim_subset_index duration_of_symp_1 duration_of_symp_2
## 1 13 1 5 0.1578976
## duration_of_symp gamma_total Beta R_0_P R_0_A R_0_S_1
## 1 5.157898 0.1938774 1.182424 1.019975 0.347037 0.880084
## R_0_S_2 R_0_NGM
## 1 0.02343045 2.270527
small_b_a_MLE_comb = small_b_a_MLE %>%
dplyr::select(-msg, -iter_num, -param_index,
-loglik, -nfail, -trace_num, -loglist.se,
-Antibody_Mean_LL, -Antibody_LL_SE,
-Median_Herd_Immunity, -combo_num,
-sim_subset_index, -duration_of_symp_1,
-duration_of_symp_2, -duration_of_symp,
-gamma_total, -Beta,
-R_0_P, -R_0_A, -R_0_S_1, -R_0_S_2,
-R_0_NGM)
##Simulation from ML
sim_data_small_b_a = simulate(nsim = 101,
seed = 12345,
times = Observed_data$times,
t0 = t0,
rprocess = pomp::euler(rproc,delta.t = 1),
params = small_b_a_MLE_comb,
paramnames = paramnames,
statenames = statenames,
obsnames = obsnames,
accumvars = acumvarnames,
rinit = init,
rmeas = rmeas,
partrans = par_trans,
covar = covar,
format = "data.frame")
sim_data_small_b_a_traj_data = sim_data_small_b_a %>%
dplyr::select(time, .id, Y)
p = ggplot(data = sim_data_small_b_a_traj_data,
aes(x = time,
y = Y,
color = .id)) +
geom_point() + geom_line(aes(group = .id)) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
rahul_man_figure_theme +
theme(legend.position = "None")
p
png("../Figures/Representative_Simulations/small_b_a_all_traj_sim.png")
print(p)
dev.off()
## quartz_off_screen
## 2
select_trajectories = filter(sim_data_small_b_a_traj_data, .id %in% seq(from = 5, to = 10))
select_trajectories = dplyr::select(select_trajectories, time, .id, Y)
select_trajectories$type = "Sim"
library(RColorBrewer)
full_blue_pallete = brewer.pal(9, "Blues")
sim_traj_pallete = full_blue_pallete[9:4]
sim_traj_scale = scale_color_manual(name = "Legend", values = c( sim_traj_pallete), labels = c("Sim_Traj_1", "Sim_Traj_2", "Sim_Traj_3", "Sim_Traj_4", "Sim_Traj_5", "Sim_Traj_6"))
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) + rahul_theme + rahul_man_figure_theme + theme_white_background +
sim_traj_scale
p
png("../Figures/Representative_Simulations/small_b_a_5_traj_sim.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) + rahul_theme +
rahul_man_figure_theme + theme_white_background +
sim_traj_scale + geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red')
p
png("../Figures/Representative_Simulations/small_b_a_5_traj_sim_with_obs.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) +
theme_white_background +
sim_traj_scale +
facet_wrap(~.id, ncol = 1) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
theme(legend.position = "None") +
theme(strip.background = element_blank(),
strip.text.x = element_blank())
p
png("../Figures/Representative_Simulations/small_b_a_5_traj_sim_with_obs_facet.png")
print(p)
dev.off()
## quartz_off_screen
## 2
p = ggplot(data = select_trajectories,
aes(x = time, y = Y, color = .id)) + geom_point(size = 2) + geom_line(aes(group = .id)) +
theme_white_background +
sim_traj_scale +
facet_wrap(~.id, ncol = 1) +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
theme(legend.position = "None")
p
(One that corresponds well with the data by eye)
small_b_a_single_traj = select_trajectories %>%
filter(.id == "5")
p = ggplot(data = small_b_a_single_traj,
aes(x = time, y = Y)) + geom_point(size = 2,
color = 'blue') +
geom_line(color = 'blue') + rahul_man_figure_theme +
geom_point(data = Observed_data,
aes(x = times, y = Y), color = 'red') +
geom_line(data = Observed_data,
aes(x = times, y = Y), color = 'red')
p
png("../Figures/Representative_Simulations/small_b_a_best_traj_vs_obs.png")
print(p)
dev.off()
## quartz_off_screen
## 2
small_b_a_single_traj_data = small_b_a_single_traj %>%
dplyr::select(Y =Y, times = time) %>%
join(nyc_rel_testing_data) %>%
mutate(obs_prop_positive = Y/Total_Number_of_Tests_Performed) %>%
dplyr::select(-Total_Number_of_Tests_Performed)
## Joining by: times
small_b_a_single_traj_data$obs_prop_positive[1] = NA
small_b_a_single_traj_data
## Y times obs_prop_positive
## 1 0 1 NA
## 2 0 2 0.00000000
## 3 3 3 0.37500000
## 4 6 4 0.16666667
## 5 1 5 0.02222222
## 6 12 6 0.18750000
## 7 4 7 0.06349206
## 8 7 8 0.05384615
## 9 7 9 0.03954802
## 10 34 10 0.19540230
## 11 28 11 0.07547170
## 12 94 12 0.13603473
## 13 173 13 0.33855186
## 14 179 14 0.21723301
## 15 329 15 0.36924804
## 16 476 16 0.21597096
## 17 1273 17 0.38068182
## 18 2050 18 0.42320396
## 19 2388 19 0.47035651
## 20 1435 20 0.20716039
## 21 1560 21 0.23586332
## 22 1732 22 0.32151476
## 23 3112 23 0.54730918
## 24 4744 24 0.68209921
## 25 2696 25 0.38942655
## 26 2704 26 0.34021137
## 27 5900 27 0.81921688
## 28 5433 28 0.87614901
## 29 3917 29 0.39597655
## 30 6875 30 0.93779839
## 31 6490 31 0.86383602
## 32 4562 32 0.48723700
## 33 5765 33 0.51253556
## 34 4623 34 0.59163041
## 35 5977 35 0.73490717
## 36 4325 36 0.48275477
## 37 7008 37 0.66288309
## 38 3601 38 0.33666791
## 39 5209 39 0.47238596
## 40 6349 40 0.53978915
## 41 2364 41 0.21923398
## 42 3607 42 0.44790761
## 43 4012 43 0.46061998
## 44 3257 44 0.20092535
## 45 6057 45 0.48363143
## 46 4975 46 0.42318816
## 47 3695 47 0.31714016
## 48 3289 48 0.32022198
## 49 2584 49 0.33136702
## 50 3370 50 0.38169668
## 51 3098 51 0.27525544
## 52 3735 52 0.27948219
## 53 2423 53 0.13375656
## 54 2157 54 0.12513053
## 55 2300 55 0.16956650
## 56 2525 56 0.23954084
## 57 1919 57 0.19399515
## 58 1848 58 0.13137129
## 59 2402 59 0.16841958
## 60 1605 60 0.11894175
## 61 1658 61 0.10077802
## 62 1585 62 0.11864661
## 63 1925 63 0.20962648
## 64 2196 64 0.20301377
## 65 2624 65 0.20113445
## 66 1331 66 0.08131224
## 67 2298 67 0.14960937
## 68 1285 68 0.08194630
## 69 1897 69 0.14496408
## 70 1399 70 0.14144171
## 71 1147 71 0.12014245
## 72 1507 72 0.10214872
## 73 757 73 0.04102981
## 74 1312 74 0.06635311
## 75 1262 75 0.06865785
## 76 910 76 0.05964866
## 77 1007 77 0.09159542
## 78 1260 78 0.09655912
## 79 1240 79 0.07954327
## 80 814 80 0.03849064
## 81 862 81 0.04486312
## 82 920 82 0.04400019
## 83 1038 83 0.04725700
## 84 662 84 0.04145013
## 85 719 85 0.04516899
## 86 1054 86 0.06014609
## 87 462 87 0.01349614
## 88 499 88 0.01463515
## 89 779 89 0.02725873
write.csv(small_b_a_single_traj_data,
"../Generated_Data/Representative_Simulations/small_b_a_single_sim_traj_data.csv",
row.names = FALSE)